Do we need a data catalog before we start governance?

No. Start with Tier 1 datasets, ownership, contracts, and automated checks. A catalog (e.g., `DataHub`, `Collibra`) becomes valuable once you’re publishing reliable metadata and lineage—otherwise you’re cataloging chaos.

What’s the fastest path to compliance improvements?

Classify sensitive fields (PII/PHI/PCI), move to default-deny access, implement masking, and ensure audit logs are retained and queryable. Those four steps typically deliver the biggest audit-risk reduction quickly.

How do we prevent governance from slowing teams down?

Make guardrails self-service: templates for contracts, standardized role modules in `Terraform`, automated CI checks with `dbt`, and JIT access approvals. Measure access request lead time and treat delays as defects.

How do you handle AI-generated data pipelines and “vibe-coded” SQL?

Wrap them in contracts and tests immediately. Then refactor incrementally: add lineage, enforce linting and PR review, and replace brittle transformations with `dbt` models plus explicit tests. The goal is to make correctness repeatable, not heroic.

Data-engineering · Jan 16, 2026 · 8 minute read

The Data Governance Framework That Finally Stopped Shipping Broken Metrics (and Got Us Through the Audit)

Governance isn’t a binder of policies. It’s the reliability layer for data products: contracts, controls, and feedback loops that keep analytics compliant, secure, and useful.

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The Data Governance Framework That Finally Stopped Shipping Broken Metrics (and Got Us Through the Audit)

Key takeaways

Implementation checklist